Hugging Face's logo

nrshoudi
/
wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
5
nrshoudi commited on
Commit
7eec9a2
•
1 Parent(s): 2915988

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +90 -0
README.md ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: facebook/wav2vec2-xls-r-300m
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # wav2vec2-large-xls-r-300m-Arabic-phoneme-based-MDD
15
+
16
+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 1.5458
19
+ - Per: 0.2292
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 0.0005
39
+ - train_batch_size: 2
40
+ - eval_batch_size: 6
41
+ - seed: 42
42
+ - gradient_accumulation_steps: 4
43
+ - total_train_batch_size: 8
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - lr_scheduler_warmup_steps: 250
47
+ - num_epochs: 30.0
48
+
49
+ ### Training results
50
+
51
+ | Training Loss | Epoch | Step | Validation Loss | Per |
52
+ |:-------------:|:-----:|:-----:|:---------------:|:------:|
53
+ | 3.2235 | 1.0 | 2187 | 1.9282 | 0.6005 |
54
+ | 1.5785 | 2.0 | 4374 | 1.4976 | 0.4575 |
55
+ | 1.2838 | 3.0 | 6561 | 1.3438 | 0.4153 |
56
+ | 1.1328 | 4.0 | 8748 | 1.3677 | 0.3842 |
57
+ | 1.0331 | 5.0 | 10935 | 1.2632 | 0.3575 |
58
+ | 0.9463 | 6.0 | 13122 | 1.2040 | 0.3515 |
59
+ | 0.8686 | 7.0 | 15309 | 1.1669 | 0.3333 |
60
+ | 0.8009 | 8.0 | 17496 | 1.1435 | 0.3233 |
61
+ | 0.748 | 9.0 | 19683 | 1.1334 | 0.3122 |
62
+ | 0.6908 | 10.0 | 21870 | 1.1131 | 0.2925 |
63
+ | 0.6339 | 11.0 | 24057 | 1.1514 | 0.2872 |
64
+ | 0.5911 | 12.0 | 26244 | 1.1188 | 0.2784 |
65
+ | 0.5513 | 13.0 | 28431 | 1.1334 | 0.2790 |
66
+ | 0.5127 | 14.0 | 30618 | 1.1662 | 0.2694 |
67
+ | 0.4782 | 15.0 | 32805 | 1.1719 | 0.2634 |
68
+ | 0.4448 | 16.0 | 34992 | 1.1635 | 0.2600 |
69
+ | 0.4167 | 17.0 | 37179 | 1.2173 | 0.2561 |
70
+ | 0.3913 | 18.0 | 39366 | 1.2252 | 0.2542 |
71
+ | 0.3639 | 19.0 | 41553 | 1.2478 | 0.2490 |
72
+ | 0.3441 | 20.0 | 43740 | 1.2977 | 0.2504 |
73
+ | 0.3245 | 21.0 | 45927 | 1.3575 | 0.2477 |
74
+ | 0.3048 | 22.0 | 48114 | 1.3489 | 0.2420 |
75
+ | 0.2885 | 23.0 | 50301 | 1.3860 | 0.2398 |
76
+ | 0.2715 | 24.0 | 52488 | 1.4116 | 0.2406 |
77
+ | 0.2588 | 25.0 | 54675 | 1.4896 | 0.2374 |
78
+ | 0.2454 | 26.0 | 56862 | 1.4672 | 0.2324 |
79
+ | 0.2344 | 27.0 | 59049 | 1.5015 | 0.2324 |
80
+ | 0.2245 | 28.0 | 61236 | 1.5205 | 0.2308 |
81
+ | 0.2183 | 29.0 | 63423 | 1.5248 | 0.2302 |
82
+ | 0.2089 | 30.0 | 65610 | 1.5458 | 0.2292 |
83
+
84
+
85
+ ### Framework versions
86
+
87
+ - Transformers 4.31.0
88
+ - Pytorch 2.0.1+cu118
89
+ - Datasets 1.18.3
90
+ - Tokenizers 0.13.3